In [1]:
#Imports
import pandas as pd
import numpy as np
import itertools
from scipy.stats import kurtosis
from sklearn.metrics import silhouette_samples, silhouette_score
import matplotlib.cm as cm
import matplotlib.pyplot as plt
from time import clock
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.cluster import KMeans
from sklearn.mixture import GaussianMixture
from collections import defaultdict
from sklearn.metrics import adjusted_mutual_info_score as ami
from sklearn.metrics import homogeneity_score, completeness_score, homogeneity_completeness_v_measure
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.decomposition import PCA
from sklearn.decomposition import FastICA
from sklearn.random_projection import SparseRandomProjection
from itertools import product
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.base import TransformerMixin,BaseEstimator
import warnings
warnings.simplefilter('ignore')
from sklearn.preprocessing import scale
Data Cleanup¶
In [2]:
def gtid():
return 903471711
def author(self):
return 'vagrawal63'
def get_income_data(file_name = "adult.csv"):
data = pd.read_csv(file_name)
#clean rows with empty values
data = data[data.occupation.str.strip() != '?']
data = data[data.workclass.str.strip() != '?']
data = data[data.nativecountry.str.strip() != '?']
#convert income to binary classes
def func(x):
if(x == " <=50K" or x == " <=50K."):
return 0
elif(x == " >50K" or x == ' >50K.'):
return 1
data['income'] = data['income'].apply(func)
#print(data.head())
result = data['income']
del data['income']
del data['education']
del data['fnlwgt']
#testing if I want to delete these additional columns
# result: Tree size drops from Cleaned: (45222, 486) to (45222, 268)
# accuracy drops from Accuracy of tree (No pruning):0.823377120460988 to 0.7842997385726206
del data['capitalgain']
del data['capitalloss']
#encode to binary values
#enc = OneHotEncoder(sparse = False, handle_unknown='ignore')
#data = enc.fit_transform(data)
data = pd.get_dummies(data, columns=['workclass','maritalstatus','occupation',
'relationship','race',
'gender','nativecountry'])
#print(enc.categories_)
data = scale(data)
return data, result
#randomize using GT ID
np.random.seed(gtid())
#Read Data File
income_data, result = get_income_data()
print("Cleaned: " + str(income_data.shape))
Cleaned: (45222, 85)
In [3]:
print("Split data")
#split data
X_train, X_test, Y_train, Y_test = train_test_split(income_data, result, test_size = 0.2)
print("X_train: " + str(X_train.shape))
Split data X_train: (36177, 85)
Part 1: K Means and GMM (Expectation Maximization)¶
In [4]:
print('Part 1: Start Clustering on Income Data ==')
clusters = [2,4,6,8,10,12,14,16,18,20,25,30]
SSE = defaultdict(dict)
BIC = defaultdict(dict)
hScore = defaultdict(dict)
cScore = defaultdict(dict)
AMI = defaultdict(dict)
VMeasure = defaultdict(dict)
km = KMeans(random_state=5)
gmm = GaussianMixture(random_state = 100)
st = clock()
for k in clusters:
km.set_params(n_clusters=k)
gmm.set_params(n_components=k)
km.fit(X_train)
gmm.fit(X_train)
SSE[k]['IncomeInertia'] = (km.inertia_)
BIC[k]['IncomeBIC'] = gmm.bic(X_train)
hScore[k]['KM'] = homogeneity_score(Y_train,km.predict(X_train))
hScore[k]['GMM'] = homogeneity_score(Y_train,gmm.predict(X_train))
cScore[k]['KM'] = completeness_score(Y_train,km.predict(X_train))
cScore[k]['GMM'] = completeness_score(Y_train,gmm.predict(X_train))
AMI[k]['KM'] = ami(Y_train,km.predict(X_train))
AMI[k]['GMM'] = ami(Y_train,gmm.predict(X_train))
a,b,vm = homogeneity_completeness_v_measure(Y_train,km.predict(X_train))
VMeasure[k]['KM'] = vm
a,b,vm = homogeneity_completeness_v_measure(Y_train,gmm.predict(X_train))
VMeasure[k]['GMM'] = vm
SSE = (pd.DataFrame(SSE)).T
BIC = pd.DataFrame(BIC).T
hScore = pd.DataFrame(hScore).T
cScore = pd.DataFrame(cScore).T
AMI = pd.DataFrame(AMI).T
VMeasure = pd.DataFrame(VMeasure).T
print("Writing files now .. ")
SSE.to_csv('./P1/IncomeClusterKMeans.csv')
BIC.to_csv('./P1/IncomeClusterGMM.csv')
hScore.to_csv('./P1/IncomeHScore.csv')
cScore.to_csv('./P1/IncomeCScore.csv')
AMI.to_csv('./P1/IncomeAMI.csv')
VMeasure.to_csv('./P1/IncomeVMeasure.csv')
print("Finished writing files")
Part 1: Start Clustering on Income Data == Writing files now .. Finished writing files
In [5]:
def plot_clustering_charts():
KNNCluster = pd.read_csv("./P1/IncomeClusterKMeans.csv", header='infer')
GMMCluster = pd.read_csv("./P1/IncomeClusterGMM.csv", header='infer')
HScore = pd.read_csv("./P1/IncomeHScore.csv", header = 'infer')
CScore = pd.read_csv("./P1/IncomeCScore.csv", header = 'infer')
AMI = pd.read_csv("./P1/IncomeAMI.csv", header = 'infer')
VMeasure = pd.read_csv("./P1/IncomeVMeasure.csv", header = 'infer')
x_data = HScore['Unnamed: 0']
plt.close()
plt.plot(x_data, KNNCluster['IncomeInertia'], 'bx-', color = 'blue', linewidth = 1, label = "Number of Clusters" )
plt.axvline(x = 6, linestyle = "--", linewidth = 1, color = "k", label = "Optimal Clusters = 6")
plt.legend(loc = 'best')
plt.title("Figure 1.1: KMeans Elbow Method\nIncome Dataset")
plt.xlabel("Number of Clusters")
plt.ylabel("Sum of Squared Distances");
plt.show()
plt.close()
plt.plot(x_data, HScore['KM'], color = 'orange', label = "Homogenity" )
plt.plot(x_data, CScore['KM'], color = 'blue', label = "Completeness" )
plt.plot(x_data, AMI['KM'], color = 'green', label = "Adjusted MI" )
plt.plot(x_data, VMeasure['KM'], color = 'red', label = "V Measure" )
plt.axvline(x = 6 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Clusters = 6")
plt.legend(loc = 'upper right')
plt.title("Figure 1.2: KMeans Performance Evaluation\nIncome Dataset")
plt.xlabel("Number of Clusters")
plt.ylabel("Score");
plt.show()
plt.close()
plt.plot(x_data, GMMCluster['IncomeBIC'], 'bx-', color = 'blue', linewidth = 1, label = "Number of Clusters" )
plt.axvline(x = 8 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Clusters = 8")
plt.legend(loc = 'best')
plt.title("Figure 2.2: Expectation Maximization BIC\nIncome Dataset")
plt.xlabel("Number of Clusters")
plt.ylabel("BIC");
plt.show()
plt.close()
plt.plot(x_data, HScore['GMM'], color = 'orange', label = "Homogenity" )
plt.plot(x_data, CScore['GMM'], color = 'blue', label = "Completeness" )
plt.plot(x_data, AMI['GMM'], color = 'green', label = "Adjusted MI" )
plt.plot(x_data, VMeasure['GMM'], color = 'red', label = "V Measure" )
plt.axvline(x = 8 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Clusters = 8")
plt.legend(loc = 'upper right')
plt.title("Figure 2.2: Expectation Maximization Performance Evaluation\nIncome Dataset")
plt.xlabel("Number of Clusters")
plt.ylabel("Score");
plt.show()
plt.close()
In [6]:
plot_clustering_charts()
In [7]:
def KM_Silhoutte(X, y, title=""):
if (title == ""):
title = "Figure 1.3: KMeans Clustering Silhoutte Analysis\nIncome Dataset "
# Generating the sample data from make_blobs
# This particular setting has one distinct cluster and 3 clusters placed close
# together.
range_n_clusters = [2,4,6,8,10,12,14,16,18,20,25,30]
for n_clusters in range_n_clusters:
# Create a subplot with 1 row and 2 columns
fig, (ax1, ax2) = plt.subplots(1, 2)
fig.set_size_inches(12,5)
# The 1st subplot is the silhouette plot
# The silhouette coefficient can range from -1, 1 but in this example all
# lie within [-0.1, 1]
ax1.set_xlim([-0.1, 1])
# The (n_clusters+1)*10 is for inserting blank space between silhouette
# plots of individual clusters, to demarcate them clearly.
ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])
# Initialize the clusterer with n_clusters value and a random generator
# seed of 10 for reproducibility.
clusterer = KMeans(n_clusters=n_clusters, random_state=10)
cluster_labels = clusterer.fit_predict(X)
# The silhouette_score gives the average value for all the samples.
# This gives a perspective into the density and separation of the formed
# clusters
silhouette_avg = silhouette_score(X, cluster_labels)
print("For n_clusters =", n_clusters,
"The average silhouette_score is :", silhouette_avg)
# Compute the silhouette scores for each sample
sample_silhouette_values = silhouette_samples(X, cluster_labels)
y_lower = 10
for i in range(n_clusters):
# Aggregate the silhouette scores for samples belonging to
# cluster i, and sort them
ith_cluster_silhouette_values = \
sample_silhouette_values[cluster_labels == i]
ith_cluster_silhouette_values.sort()
size_cluster_i = ith_cluster_silhouette_values.shape[0]
y_upper = y_lower + size_cluster_i
color = cm.nipy_spectral(float(i) / n_clusters)
ax1.fill_betweenx(np.arange(y_lower, y_upper),
0, ith_cluster_silhouette_values,
facecolor=color, edgecolor=color, alpha=0.7)
# Label the silhouette plots with their cluster numbers at the middle
ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))
# Compute the new y_lower for next plot
y_lower = y_upper + 10 # 10 for the 0 samples
#ax1.set_title("Figure 1c: kMeans silhouette plot (Cancer Dataset)")
ax1.set_xlabel("Silhouette coefficient values")
#ax1.set_ylabel("Cluster label")
# The vertical line for average silhouette score of all the values
ax1.axvline(x=silhouette_avg, color="red", linestyle="--", label="Average Silhouette Score")
#ax1.set_yticks([]) # Clear the yaxis labels / ticks
#ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])
# 2nd Plot showing the actual clusters formed
colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)
ax2.scatter(X[:, 0], X[:, 1], marker='.', s=30, lw=0, alpha=0.7,
c=colors, edgecolor='k')
# Labeling the clusters
centers = clusterer.cluster_centers_
# Draw white circles at cluster centers
ax2.scatter(centers[:, 0], centers[:, 1], marker='o',
c="white", alpha=1, s=200, edgecolor='k')
for i, c in enumerate(centers):
ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,
s=50, edgecolor='k')
plt.suptitle((title+
"(No of Clusters = %d)" % n_clusters),
fontsize=14)
plt.show()
In [8]:
KM_Silhoutte(X_train, Y_train)
For n_clusters = 2 The average silhouette_score is : 0.08115581078907247 For n_clusters = 4 The average silhouette_score is : 0.10272281993615101 For n_clusters = 6 The average silhouette_score is : 0.07091988464127319 For n_clusters = 8 The average silhouette_score is : 0.0783204371498665 For n_clusters = 10 The average silhouette_score is : 0.06736107980806884 For n_clusters = 12 The average silhouette_score is : 0.08830071308883625 For n_clusters = 14 The average silhouette_score is : 0.10042991998709964 For n_clusters = 16 The average silhouette_score is : 0.0990647039652664 For n_clusters = 18 The average silhouette_score is : 0.09380364800221486 For n_clusters = 20 The average silhouette_score is : 0.10985374451896808 For n_clusters = 25 The average silhouette_score is : 0.11028857569880582 For n_clusters = 30 The average silhouette_score is : 0.10099765925463926
In [9]:
def compute_bic_score(X,title1, title2):
lowest_bic = np.infty
bic = []
n_components_range = [2,4,6,8,10,12,14,16,18,20,25,30]
cv_types = ['spherical', 'tied', 'diag', 'full']
for cv_type in cv_types:
for n_components in n_components_range:
# Fit a Gaussian mixture with EM
gmm = GaussianMixture(n_components=n_components,
covariance_type=cv_type)
gmm.fit(X)
bic.append(gmm.bic(X))
print("CV Type: ", cv_type, " Components: ", n_components, " BIC Score: ", bic[-1])
if bic[-1] < lowest_bic:
lowest_bic = bic[-1]
best_gmm = gmm
bic = np.array(bic)
color_iter = itertools.cycle(['navy', 'turquoise', 'cornflowerblue',
'darkorange'])
print("Lowest BIC score = ", lowest_bic)
# Plot the BIC scores
plt.figure(figsize=(8, 6))
clf = best_gmm
bars = []
spl = plt.subplot(2, 1, 1)
for i, (cv_type, color) in enumerate(zip(cv_types, color_iter)):
xpos = np.array(n_components_range) + .2 * (i - 2)
bars.append(plt.bar(xpos, bic[i * len(n_components_range):
(i + 1) * len(n_components_range)],
width=.2, color=color))
plt.xticks(n_components_range)
plt.ylim([bic.min() * 1.01 - .01 * bic.max(), bic.max()])
plt.title(title1)
xpos = np.mod(bic.argmin(), len(n_components_range)) + .65 +\
.2 * np.floor(bic.argmin() / len(n_components_range))
plt.text(xpos, bic.min() * 0.97 + .03 * bic.max(), '*', fontsize=14)
spl.set_xlabel('Number of components')
spl.legend([b[0] for b in bars], cv_types)
#plt.show()
#plt.close()
# Plot the winner
#4splot = plt.subplot(2, 1, 2)
Y_ = clf.predict(X)
plt.show()
plt.close()
In [10]:
compute_bic_score(X_train, "Figure 2.1: Expectation Maximization BIC Score\nIncome Dataset" , "Figure 2.2: Cluster Representation\nIncome Dataset")
CV Type: spherical Components: 2 BIC Score: 6986357.822452916 CV Type: spherical Components: 4 BIC Score: 6434361.585392872 CV Type: spherical Components: 6 BIC Score: 6516814.151453002 CV Type: spherical Components: 8 BIC Score: 6084228.923079217 CV Type: spherical Components: 10 BIC Score: 5762750.719819664 CV Type: spherical Components: 12 BIC Score: 5720984.73560336 CV Type: spherical Components: 14 BIC Score: 5845278.291031575 CV Type: spherical Components: 16 BIC Score: 5814403.920487542 CV Type: spherical Components: 18 BIC Score: 5557846.257856089 CV Type: spherical Components: 20 BIC Score: 5724045.86817732 CV Type: spherical Components: 25 BIC Score: 5533624.2194878 CV Type: spherical Components: 30 BIC Score: 4992626.243944958 CV Type: tied Components: 2 BIC Score: 4443030.446435416 CV Type: tied Components: 4 BIC Score: 4295826.503749669 CV Type: tied Components: 6 BIC Score: 4656025.341227304 CV Type: tied Components: 8 BIC Score: 2937270.0103856977 CV Type: tied Components: 10 BIC Score: 1601168.68235255 CV Type: tied Components: 12 BIC Score: 2629259.741099345 CV Type: tied Components: 14 BIC Score: 725136.7080060458 CV Type: tied Components: 16 BIC Score: 1579812.9839806214 CV Type: tied Components: 18 BIC Score: 46965.4972728387 CV Type: tied Components: 20 BIC Score: -147660.81256293668 CV Type: tied Components: 25 BIC Score: -2088045.315300413 CV Type: tied Components: 30 BIC Score: -4294773.471039979 CV Type: diag Components: 2 BIC Score: 8039116.821840139 CV Type: diag Components: 4 BIC Score: -125571.64819110215 CV Type: diag Components: 6 BIC Score: -4522064.606861852 CV Type: diag Components: 8 BIC Score: -9676384.758504674 CV Type: diag Components: 10 BIC Score: -2582571.3004599446 CV Type: diag Components: 12 BIC Score: -13314191.234770233 CV Type: diag Components: 14 BIC Score: -4561076.640439638 CV Type: diag Components: 16 BIC Score: -10011690.124383181 CV Type: diag Components: 18 BIC Score: -15276641.511928806 CV Type: diag Components: 20 BIC Score: -11398187.600016959 CV Type: diag Components: 25 BIC Score: -11840548.919495095 CV Type: diag Components: 30 BIC Score: -10692287.231013007 CV Type: full Components: 2 BIC Score: 2274996.9475946077 CV Type: full Components: 4 BIC Score: -2328061.1720435675 CV Type: full Components: 6 BIC Score: -4396561.999375262 CV Type: full Components: 8 BIC Score: -14623674.174679583 CV Type: full Components: 10 BIC Score: -19620935.228373975 CV Type: full Components: 12 BIC Score: -10996908.48428336 CV Type: full Components: 14 BIC Score: -7592777.625265727 CV Type: full Components: 16 BIC Score: -12177894.082685187 CV Type: full Components: 18 BIC Score: -18229994.809218112 CV Type: full Components: 20 BIC Score: -13193306.098539768 CV Type: full Components: 25 BIC Score: -11691504.901464531 CV Type: full Components: 30 BIC Score: -13281408.258502735 Lowest BIC score = -19620935.228373975
Part 2: Dimensionality Reduction (PCA, ICA, RP and SVD)¶
In [24]:
dimensions = range(1, 85)
ann_learning_rate = [0.05]
ann_hidden_layers = [(8)]
def run_ann(dimensions, classifier, X, Y):
grid ={'clf__n_components':dimensions,'NN__learning_rate_init':ann_learning_rate,'NN__hidden_layer_sizes':ann_hidden_layers}
ann = MLPClassifier(activation='logistic',max_iter=2000,early_stopping=True,random_state=5)
pipe = Pipeline([('clf',classifier),('NN',ann)])
gs = GridSearchCV(pipe,grid,verbose=2,cv=5)
gs.fit(X, Y)
return (pd.DataFrame(gs.cv_results_) , gs.best_estimator_)
In [25]:
print('Part 2: PCA for Income dataset')
pca = PCA(random_state = 5)
pca.fit_transform(X_train)
EVR = pd.Series(data = pca.explained_variance_ratio_,index = range(0,85))
EVR.to_csv('./P2/IncomePCA-EVR.csv')
EV = pd.Series(data = pca.explained_variance_,index = range(0,85))
EV.to_csv('./P2/IncomePCA-EV.csv')
pca = PCA(random_state = 5)
nn_results, clf = run_ann(dimensions, pca, X_train, Y_train)
nn_results.to_csv('./P2/IncomePCA_ANN.csv')
test_score = clf.score(X_test, Y_test)
Part 2: PCA for Income dataset Fitting 5 folds for each of 84 candidates, totalling 420 fits [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 1.3s remaining: 0.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1, total= 0.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5, total= 0.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7, total= 0.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8, total= 0.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9, total= 0.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10, total= 0.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10, total= 0.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12, total= 0.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12, total= 0.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13, total= 0.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14, total= 0.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14, total= 0.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14, total= 0.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15, total= 0.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16, total= 0.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16, total= 0.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17, total= 0.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19, total= 0.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21, total= 0.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22, total= 2.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26, total= 2.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30, total= 2.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32, total= 2.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34, total= 2.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36, total= 0.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37, total= 2.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42, total= 2.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46, total= 2.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48, total= 2.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49, total= 2.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57, total= 2.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57, total= 2.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57, total= 2.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58, total= 2.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58, total= 2.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58, total= 2.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58, total= 2.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59, total= 2.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59, total= 2.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60, total= 2.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61, total= 2.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61, total= 2.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61, total= 2.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61, total= 2.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62, total= 2.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62, total= 2.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62, total= 2.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63, total= 2.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63, total= 2.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63, total= 2.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64, total= 2.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78, total= 2.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79, total= 2.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84, total= 1.7s
[Parallel(n_jobs=1)]: Done 420 out of 420 | elapsed: 10.6min finished
Test Accuracy : 0.8308457711442786
Best Estimator : Pipeline(memory=None,
steps=[('clf',
PCA(copy=True, iterated_power='auto', n_components=43,
random_state=5, svd_solver='auto', tol=0.0,
whiten=False)),
('NN',
MLPClassifier(activation='logistic', alpha=0.0001,
batch_size='auto', beta_1=0.9, beta_2=0.999,
early_stopping=True, epsilon=1e-08,
hidden_layer_sizes=8, learning_rate='constant',
learning_rate_init=0.05, max_iter=2000,
momentum=0.9, n_iter_no_change=10,
nesterovs_momentum=True, power_t=0.5,
random_state=5, shuffle=True, solver='adam',
tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False))],
verbose=False)
In [26]:
EVR.plot( ylim = (0.,0.35), color = 'red', label = "Variance Ratio" )
ax = EV.plot(kind = 'bar',ylim = (0.,1.0),label = "Eigen Values")
ticks = ax.xaxis.get_ticklocs()
ticklabels = [l.get_text() for l in ax.xaxis.get_ticklabels()]
ax.xaxis.set_ticks(ticks[::10])
ax.xaxis.set_ticklabels(ticklabels[::10]);
ax.axvline(x = 48 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Components")
plt.legend(loc=1)
plt.title("Figure 3.1: PCA Eigen Values\nIncome Dataset")
plt.xlabel("Principal Components")
plt.ylabel("Variance Ratio");
plt.show()
plt.close()
print("Reduced Dimension: {} out of {}".
format(X_train.shape[1]-len([i for i in EVR if i >= 0.025]),X_train.shape[1]))
print("Variance captured: {} %".format(sum([i for i in EVR if i >= 0.025])*100.))
nn_pca = pd.read_csv("./P2/IncomePCA_ANN.csv", header = 'infer')
nn_pca = nn_results['mean_test_score'] * 100.0
nn_pca.plot( color = 'blue', label = "ANN Accuracy" )
plt.axvline(x = 48 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Components (n=48)")
plt.legend(loc='best')
plt.title("Figure 3.2: ANN Accuracy Dimension Reduction using PCA\nIncome Dataset")
plt.xlabel("Principal Components")
plt.ylabel("Accuracy Percentage");
plt.show()
plt.close()
Reduced Dimension: 82 out of 85 Variance captured: 11.169802345352783 %
In [27]:
ica = FastICA(random_state=5)
temp = ica.fit_transform(X_train)
order = [-abs(kurtosis(temp[:,i])) for i in range(temp.shape[1])]
temp = temp[:,np.array(order).argsort()]
kurt = pd.Series([abs(kurtosis(temp[:,i])) for i in range(temp.shape[1])]);
ica = FastICA(random_state=5)
nn_results , clf = run_ann(dimensions, ica, X_train, Y_train)
nn_results.to_csv('./P2/IncomeICA_ANN.csv')
test_score = clf.score(X_test, Y_test)
Fitting 5 folds for each of 84 candidates, totalling 420 fits [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 1.5s remaining: 0.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12, total= 1.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14, total= 1.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20, total= 1.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22, total= 1.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23, total= 2.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24, total= 2.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25, total= 2.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26, total= 1.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27, total= 2.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28, total= 1.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29, total= 2.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31, total= 2.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31, total= 2.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32, total= 2.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34, total= 2.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35, total= 1.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35, total= 2.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36, total= 2.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36, total= 2.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38, total= 2.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38, total= 2.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39, total= 2.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39, total= 2.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39, total= 1.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39, total= 1.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40, total= 2.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40, total= 2.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40, total= 2.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41, total= 2.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41, total= 1.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41, total= 2.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41, total= 2.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42, total= 2.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42, total= 2.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42, total= 2.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42, total= 2.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43, total= 2.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44, total= 2.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44, total= 2.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44, total= 2.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44, total= 2.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45, total= 2.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45, total= 2.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45, total= 2.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46, total= 2.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46, total= 2.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46, total= 2.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46, total= 2.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47, total= 2.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47, total= 2.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47, total= 2.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47, total= 2.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48, total= 2.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48, total= 2.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48, total= 2.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49, total= 2.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49, total= 2.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50, total= 2.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50, total= 2.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50, total= 2.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50, total= 2.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51, total= 4.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51, total= 2.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52, total= 2.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52, total= 2.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52, total= 2.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52, total= 2.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53, total= 2.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54, total= 2.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54, total= 4.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54, total= 2.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54, total= 2.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55, total= 2.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55, total= 2.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55, total= 2.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56, total= 2.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57, total= 2.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57, total= 2.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57, total= 4.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58, total= 2.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58, total= 2.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59, total= 4.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59, total= 4.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59, total= 2.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59, total= 2.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60, total= 4.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61, total= 4.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61, total= 4.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62, total= 2.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62, total= 2.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63, total= 4.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63, total= 5.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63, total= 2.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64, total= 4.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64, total= 2.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65, total= 4.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66, total= 4.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67, total= 4.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67, total= 4.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68, total= 4.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68, total= 4.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69, total= 4.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69, total= 4.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69, total= 4.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69, total= 4.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70, total= 4.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72, total= 4.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72, total= 5.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73, total= 4.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73, total= 4.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74, total= 4.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74, total= 4.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74, total= 5.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75, total= 4.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75, total= 4.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75, total= 4.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75, total= 4.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76, total= 4.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76, total= 4.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76, total= 4.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77, total= 4.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77, total= 4.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77, total= 4.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77, total= 4.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77, total= 4.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78, total= 4.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78, total= 4.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78, total= 5.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79, total= 4.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79, total= 4.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79, total= 4.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79, total= 4.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79, total= 8.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80, total= 8.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80, total= 8.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80, total= 8.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80, total= 8.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80, total= 8.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81, total= 8.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81, total= 8.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81, total= 8.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81, total= 8.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81, total= 9.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82, total= 8.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82, total= 8.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82, total= 8.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82, total= 8.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82, total= 8.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83, total= 8.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83, total= 8.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83, total= 8.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83, total= 8.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83, total= 9.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84, total= 8.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84, total= 8.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84, total= 8.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84, total= 8.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84, total= 8.3s
[Parallel(n_jobs=1)]: Done 420 out of 420 | elapsed: 20.2min finished
Test Accuracy : 0.8323935876174682
Best Estimator : Pipeline(memory=None,
steps=[('clf',
FastICA(algorithm='parallel', fun='logcosh', fun_args=None,
max_iter=200, n_components=81, random_state=5,
tol=0.0001, w_init=None, whiten=True)),
('NN',
MLPClassifier(activation='logistic', alpha=0.0001,
batch_size='auto', beta_1=0.9, beta_2=0.999,
early_stopping=True, epsilon=1e-08,
hidden_layer_sizes=8, learning_rate='constant',
learning_rate_init=0.05, max_iter=2000,
momentum=0.9, n_iter_no_change=10,
nesterovs_momentum=True, power_t=0.5,
random_state=5, shuffle=True, solver='adam',
tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False))],
verbose=False)
In [28]:
plt.figure(figsize=(6,4))
ax = kurt.plot(kind = 'bar', label = "Kurtosis Disribution");
ticks = ax.xaxis.get_ticklocs()
ticklabels = [l.get_text() for l in ax.xaxis.get_ticklabels()]
ax.xaxis.set_ticks(ticks[::10])
ax.xaxis.set_ticklabels(ticklabels[::10]);
ax.axvline(x=33 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Components")
plt.legend(loc='best')
plt.title("Figure 4.1: ICA - Kurtosis\nIncome Dataset")
plt.xlabel("Independent Components")
plt.ylabel("Kurtosis");
plt.show()
plt.close()
print("Reduced Dimension: {} out of {}".format(X_train.shape[1]-len([i for i in kurt if i >= 8.]),
X_train.shape[1]))
nn_ica = pd.read_csv("./P2/IncomeICA_ANN.csv", header = 'infer')
nn_ica = nn_results['mean_test_score'] * 100.0
plt.axvline(x=33 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Components (n=33)")
nn_ica.plot( color = 'blue', label = "ANN Accuracy" )
plt.legend(loc='best')
plt.title("Figure 4.2: ANN Accuracy Dimension Reduction using ICA\nIncome Dataset")
plt.xlabel("Independent Components")
plt.ylabel("Accuracy Percentage");
plt.show()
plt.close()
Reduced Dimension: 15 out of 85
In [ ]:
def distance_correlation (X1,X2):
assert X1.shape[0] == X2.shape[0]
return np.corrcoef(pairwise_distances(X1).ravel(),pairwise_distances(X2).ravel())[0,1]
In [ ]:
tmp = defaultdict(dict)
for i,dim in product(range(10),dimensions):
rp = SparseRandomProjection(random_state=i, n_components=dim)
tmp[dim][i] = distance_correlation(rp.fit_transform(X_train), X_train)
tmp = pd.DataFrame(tmp).T
tmp.to_csv('./P2/IncomeRP_DistanceCorrelation.csv')
In [ ]:
# Run Neural Networks
rp = SparseRandomProjection(random_state=5)
nn_results, clf = run_ann(dimensions, rp, X_train, Y_train)
nn_results.to_csv('./P2/IncomeRP_ANN.csv')
## test score
test_score = clf.score(X_test, Y_test)
print("Test Accuracy = ", test_score )
print("Best Estimator = ", clf)
In [ ]:
tmp['mean'] = tmp.mean(axis=1)
distance = tmp['mean']*100.0
distance.plot(color = 'blue', label = "Distance Correlation" )
plt.axvline(x=8 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Features")
plt.legend(loc='best')
plt.title("Figure 5.1: Random Projection Distance Correlation\nIncome Dataset")
plt.xlabel("Random Components")
plt.ylabel("Distance Correlation");
plt.show()
plt.close()
nn_results = pd.read_csv("./P2/IncomeRP_ANN.csv", header = 'infer')
nn_results = nn_results['mean_test_score'] * 100.0
nn_results.plot( color = 'blue', label = "Accuracy Percentage" )
plt.axvline(x=8 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Features (n=8)")
plt.legend(loc='best')
plt.title("Figure 5.2: ANN Accuracy Dimension Reduction using RP\nIncome Dataset")
plt.xlabel("Random Components")
plt.ylabel("Accuracy Percentage");
plt.show()
plt.close()
In [16]:
class ImportanceSelect(BaseEstimator, TransformerMixin):
def __init__(self, model, n=1):
self.model = model
self.n = n
def fit(self, *args, **kwargs):
self.model.fit(*args, **kwargs)
return self
def transform(self, X):
return X[:,self.model.feature_importances_.argsort()[::-1][:self.n]]
In [17]:
rfc = RandomForestClassifier(n_estimators=100, class_weight='balanced', random_state=5, n_jobs=-1)
result = rfc.fit(X_train, Y_train).feature_importances_
tmp = pd.Series(np.sort(result)[::-1])
tmp.to_csv('./P2/IncomeRF_FI.csv')
In [18]:
ann_learning_rate = [0.05]
ann_hidden_layers = [(8)]
rfc = RandomForestClassifier(n_estimators=100,class_weight='balanced',random_state=5,n_jobs=-1)
filtr = ImportanceSelect(rfc)
grid ={'filter__n':dimensions,'NN__learning_rate_init':ann_learning_rate,'NN__hidden_layer_sizes':ann_hidden_layers}
ann = MLPClassifier(activation='logistic',max_iter=2000,early_stopping=True,random_state=5)
pipe = Pipeline([('filter',filtr),('NN',ann)])
gs = GridSearchCV(pipe,grid,verbose=10,cv=5)
gs.fit(X_train, Y_train)
nn_results = pd.DataFrame(gs.cv_results_)
nn_results.to_csv('./P2/IncomeRF_ANN.csv')
Fitting 5 folds for each of 84 candidates, totalling 420 fits [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=1
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=1, score=0.752, total= 4.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=1
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 4.7s remaining: 0.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=1, score=0.752, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=1
[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 7.9s remaining: 0.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=1, score=0.752, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=1
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 11.3s remaining: 0.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=1, score=0.752, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=1
[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 14.6s remaining: 0.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=1, score=0.752, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=2
[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 17.7s remaining: 0.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=2, score=0.788, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=2
[Parallel(n_jobs=1)]: Done 6 out of 6 | elapsed: 20.9s remaining: 0.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=2, score=0.777, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=2
[Parallel(n_jobs=1)]: Done 7 out of 7 | elapsed: 24.4s remaining: 0.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=2, score=0.779, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=2
[Parallel(n_jobs=1)]: Done 8 out of 8 | elapsed: 28.1s remaining: 0.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=2, score=0.787, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=2
[Parallel(n_jobs=1)]: Done 9 out of 9 | elapsed: 31.6s remaining: 0.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=2, score=0.788, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=3 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=3, score=0.795, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=3 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=3, score=0.789, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=3 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=3, score=0.785, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=3 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=3, score=0.791, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=3 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=3, score=0.798, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=4 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=4, score=0.823, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=4 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=4, score=0.821, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=4 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=4, score=0.826, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=4 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=4, score=0.821, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=4 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=4, score=0.829, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=5 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=5, score=0.823, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=5 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=5, score=0.818, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=5 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=5, score=0.827, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=5 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=5, score=0.825, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=5 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=5, score=0.829, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=6 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=6, score=0.820, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=6 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=6, score=0.819, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=6 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=6, score=0.824, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=6 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=6, score=0.825, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=6 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=6, score=0.833, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=7 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=7, score=0.827, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=7 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=7, score=0.820, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=7 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=7, score=0.822, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=7 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=7, score=0.822, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=7 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=7, score=0.832, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=8 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=8, score=0.828, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=8 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=8, score=0.828, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=8 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=8, score=0.829, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=8 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=8, score=0.818, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=8 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=8, score=0.835, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=9 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=9, score=0.827, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=9 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=9, score=0.821, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=9 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=9, score=0.824, total= 2.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=9 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=9, score=0.820, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=9 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=9, score=0.835, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=10 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=10, score=0.828, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=10 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=10, score=0.827, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=10 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=10, score=0.824, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=10 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=10, score=0.818, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=10 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=10, score=0.836, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=11 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=11, score=0.822, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=11 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=11, score=0.826, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=11 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=11, score=0.819, total= 2.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=11 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=11, score=0.821, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=11 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=11, score=0.833, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=12 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=12, score=0.830, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=12 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=12, score=0.826, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=12 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=12, score=0.825, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=12 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=12, score=0.820, total= 4.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=12 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=12, score=0.835, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=13 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=13, score=0.831, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=13 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=13, score=0.827, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=13 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=13, score=0.829, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=13 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=13, score=0.824, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=13 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=13, score=0.836, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=14 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=14, score=0.826, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=14 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=14, score=0.828, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=14 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=14, score=0.828, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=14 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=14, score=0.822, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=14 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=14, score=0.835, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=15 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=15, score=0.828, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=15 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=15, score=0.828, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=15 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=15, score=0.826, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=15 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=15, score=0.823, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=15 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=15, score=0.837, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=16 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=16, score=0.828, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=16 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=16, score=0.827, total= 4.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=16 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=16, score=0.826, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=16 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=16, score=0.827, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=16 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=16, score=0.836, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=17 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=17, score=0.830, total= 4.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=17 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=17, score=0.824, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=17 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=17, score=0.824, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=17 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=17, score=0.828, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=17 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=17, score=0.835, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=18 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=18, score=0.832, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=18 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=18, score=0.822, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=18 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=18, score=0.827, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=18 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=18, score=0.825, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=18 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=18, score=0.837, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=19 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=19, score=0.829, total= 3.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=19 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=19, score=0.829, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=19 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=19, score=0.828, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=19 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=19, score=0.827, total= 4.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=19 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=19, score=0.839, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=20 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=20, score=0.832, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=20 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=20, score=0.825, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=20 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=20, score=0.830, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=20 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=20, score=0.826, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=20 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=20, score=0.839, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=21 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=21, score=0.830, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=21 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=21, score=0.827, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=21 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=21, score=0.830, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=21 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=21, score=0.829, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=21 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=21, score=0.836, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=22 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=22, score=0.830, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=22 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=22, score=0.827, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=22 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=22, score=0.829, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=22 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=22, score=0.829, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=22 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=22, score=0.837, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=23 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=23, score=0.829, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=23 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=23, score=0.824, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=23 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=23, score=0.831, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=23 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=23, score=0.822, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=23 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=23, score=0.839, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=24 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=24, score=0.828, total= 4.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=24 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=24, score=0.823, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=24 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=24, score=0.830, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=24 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=24, score=0.828, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=24 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=24, score=0.837, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=25 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=25, score=0.833, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=25 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=25, score=0.823, total= 4.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=25 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=25, score=0.828, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=25 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=25, score=0.826, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=25 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=25, score=0.835, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=26 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=26, score=0.830, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=26 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=26, score=0.829, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=26 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=26, score=0.831, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=26 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=26, score=0.826, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=26 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=26, score=0.837, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=27 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=27, score=0.830, total= 4.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=27 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=27, score=0.823, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=27 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=27, score=0.828, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=27 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=27, score=0.827, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=27 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=27, score=0.831, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=28 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=28, score=0.832, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=28 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=28, score=0.828, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=28 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=28, score=0.827, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=28 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=28, score=0.826, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=28 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=28, score=0.832, total= 4.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=29 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=29, score=0.833, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=29 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=29, score=0.828, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=29 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=29, score=0.831, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=29 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=29, score=0.829, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=29 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=29, score=0.832, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=30 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=30, score=0.828, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=30 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=30, score=0.824, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=30 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=30, score=0.831, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=30 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=30, score=0.825, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=30 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=30, score=0.836, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=31 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=31, score=0.832, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=31 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=31, score=0.825, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=31 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=31, score=0.831, total= 4.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=31 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=31, score=0.825, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=31 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=31, score=0.838, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=32 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=32, score=0.832, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=32 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=32, score=0.827, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=32 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=32, score=0.832, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=32 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=32, score=0.826, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=32 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=32, score=0.838, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=33 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=33, score=0.833, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=33 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=33, score=0.825, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=33 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=33, score=0.833, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=33 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=33, score=0.831, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=33 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=33, score=0.837, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=34 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=34, score=0.832, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=34 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=34, score=0.825, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=34 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=34, score=0.831, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=34 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=34, score=0.828, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=34 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=34, score=0.838, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=35 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=35, score=0.833, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=35 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=35, score=0.826, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=35 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=35, score=0.830, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=35 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=35, score=0.827, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=35 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=35, score=0.837, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=36 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=36, score=0.830, total= 4.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=36 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=36, score=0.826, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=36 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=36, score=0.831, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=36 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=36, score=0.825, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=36 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=36, score=0.840, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=37 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=37, score=0.833, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=37 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=37, score=0.822, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=37 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=37, score=0.828, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=37 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=37, score=0.827, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=37 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=37, score=0.836, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=38 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=38, score=0.830, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=38 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=38, score=0.825, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=38 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=38, score=0.829, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=38 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=38, score=0.822, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=38 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=38, score=0.837, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=39 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=39, score=0.833, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=39 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=39, score=0.826, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=39 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=39, score=0.832, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=39 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=39, score=0.829, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=39 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=39, score=0.840, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=40 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=40, score=0.833, total= 4.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=40 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=40, score=0.824, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=40 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=40, score=0.831, total= 4.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=40 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=40, score=0.824, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=40 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=40, score=0.831, total= 4.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=41 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=41, score=0.830, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=41 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=41, score=0.827, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=41 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=41, score=0.833, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=41 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=41, score=0.832, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=41 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=41, score=0.835, total= 4.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=42 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=42, score=0.829, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=42 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=42, score=0.826, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=42 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=42, score=0.830, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=42 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=42, score=0.829, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=42 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=42, score=0.835, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=43 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=43, score=0.835, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=43 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=43, score=0.826, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=43 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=43, score=0.829, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=43 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=43, score=0.830, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=43 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=43, score=0.835, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=44 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=44, score=0.830, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=44 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=44, score=0.826, total= 4.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=44 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=44, score=0.833, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=44 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=44, score=0.829, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=44 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=44, score=0.839, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=45 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=45, score=0.830, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=45 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=45, score=0.827, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=45 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=45, score=0.829, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=45 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=45, score=0.828, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=45 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=45, score=0.835, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=46 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=46, score=0.825, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=46 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=46, score=0.827, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=46 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=46, score=0.831, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=46 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=46, score=0.829, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=46 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=46, score=0.835, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=47 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=47, score=0.832, total= 4.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=47 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=47, score=0.828, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=47 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=47, score=0.829, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=47 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=47, score=0.831, total= 4.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=47 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=47, score=0.837, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=48 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=48, score=0.831, total= 4.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=48 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=48, score=0.824, total= 4.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=48 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=48, score=0.827, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=48 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=48, score=0.827, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=48 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=48, score=0.834, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=49 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=49, score=0.836, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=49 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=49, score=0.824, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=49 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=49, score=0.832, total= 4.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=49 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=49, score=0.822, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=49 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=49, score=0.834, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=50 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=50, score=0.831, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=50 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=50, score=0.822, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=50 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=50, score=0.830, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=50 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=50, score=0.830, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=50 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=50, score=0.834, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=51 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=51, score=0.829, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=51 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=51, score=0.825, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=51 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=51, score=0.832, total= 4.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=51 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=51, score=0.830, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=51 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=51, score=0.839, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=52 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=52, score=0.832, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=52 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=52, score=0.825, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=52 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=52, score=0.831, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=52 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=52, score=0.831, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=52 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=52, score=0.837, total= 4.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=53 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=53, score=0.827, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=53 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=53, score=0.824, total= 4.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=53 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=53, score=0.827, total= 4.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=53 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=53, score=0.831, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=53 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=53, score=0.835, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=54 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=54, score=0.829, total= 4.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=54 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=54, score=0.828, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=54 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=54, score=0.829, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=54 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=54, score=0.825, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=54 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=54, score=0.831, total= 4.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=55 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=55, score=0.833, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=55 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=55, score=0.828, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=55 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=55, score=0.831, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=55 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=55, score=0.825, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=55 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=55, score=0.837, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=56 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=56, score=0.831, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=56 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=56, score=0.818, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=56 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=56, score=0.833, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=56 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=56, score=0.822, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=56 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=56, score=0.837, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=57 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=57, score=0.832, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=57 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=57, score=0.825, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=57 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=57, score=0.833, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=57 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=57, score=0.832, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=57 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=57, score=0.835, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=58 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=58, score=0.827, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=58 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=58, score=0.824, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=58 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=58, score=0.825, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=58 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=58, score=0.825, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=58 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=58, score=0.836, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=59 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=59, score=0.829, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=59 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=59, score=0.823, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=59 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=59, score=0.831, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=59 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=59, score=0.829, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=59 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=59, score=0.834, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=60 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=60, score=0.827, total= 4.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=60 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=60, score=0.826, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=60 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=60, score=0.826, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=60 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=60, score=0.829, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=60 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=60, score=0.834, total= 4.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=61 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=61, score=0.830, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=61 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=61, score=0.823, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=61 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=61, score=0.833, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=61 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=61, score=0.828, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=61 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=61, score=0.837, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=62 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=62, score=0.829, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=62 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=62, score=0.826, total= 4.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=62 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=62, score=0.830, total= 4.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=62 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=62, score=0.830, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=62 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=62, score=0.839, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=63 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=63, score=0.829, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=63 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=63, score=0.825, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=63 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=63, score=0.831, total= 4.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=63 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=63, score=0.831, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=63 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=63, score=0.834, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=64 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=64, score=0.836, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=64 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=64, score=0.825, total= 4.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=64 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=64, score=0.835, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=64 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=64, score=0.829, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=64 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=64, score=0.836, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=65 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=65, score=0.829, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=65 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=65, score=0.825, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=65 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=65, score=0.834, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=65 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=65, score=0.831, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=65 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=65, score=0.835, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=66 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=66, score=0.830, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=66 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=66, score=0.824, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=66 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=66, score=0.827, total= 4.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=66 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=66, score=0.826, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=66 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=66, score=0.834, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=67 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=67, score=0.831, total= 4.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=67 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=67, score=0.823, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=67 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=67, score=0.827, total= 4.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=67 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=67, score=0.826, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=67 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=67, score=0.832, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=68 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=68, score=0.829, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=68 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=68, score=0.824, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=68 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=68, score=0.832, total= 3.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=68 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=68, score=0.822, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=68 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=68, score=0.835, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=69 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=69, score=0.831, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=69 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=69, score=0.825, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=69 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=69, score=0.827, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=69 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=69, score=0.826, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=69 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=69, score=0.834, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=70 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=70, score=0.827, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=70 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=70, score=0.828, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=70 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=70, score=0.831, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=70 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=70, score=0.825, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=70 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=70, score=0.834, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=71 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=71, score=0.832, total= 4.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=71 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=71, score=0.821, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=71 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=71, score=0.830, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=71 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=71, score=0.824, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=71 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=71, score=0.835, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=72 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=72, score=0.831, total= 3.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=72 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=72, score=0.825, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=72 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=72, score=0.831, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=72 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=72, score=0.825, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=72 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=72, score=0.832, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=73 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=73, score=0.829, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=73 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=73, score=0.826, total= 4.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=73 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=73, score=0.829, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=73 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=73, score=0.825, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=73 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=73, score=0.833, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=74 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=74, score=0.828, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=74 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=74, score=0.827, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=74 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=74, score=0.831, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=74 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=74, score=0.827, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=74 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=74, score=0.839, total= 4.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=75 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=75, score=0.827, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=75 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=75, score=0.824, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=75 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=75, score=0.828, total= 5.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=75 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=75, score=0.828, total= 5.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=75 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=75, score=0.837, total= 4.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=76 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=76, score=0.833, total= 4.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=76 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=76, score=0.824, total= 4.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=76 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=76, score=0.831, total= 4.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=76 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=76, score=0.825, total= 4.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=76 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=76, score=0.836, total= 4.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=77 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=77, score=0.827, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=77 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=77, score=0.823, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=77 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=77, score=0.830, total= 4.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=77 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=77, score=0.826, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=77 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=77, score=0.838, total= 4.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=78 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=78, score=0.828, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=78 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=78, score=0.824, total= 4.1s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=78 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=78, score=0.833, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=78 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=78, score=0.828, total= 4.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=78 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=78, score=0.832, total= 4.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=79 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=79, score=0.828, total= 4.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=79 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=79, score=0.822, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=79 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=79, score=0.831, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=79 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=79, score=0.824, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=79 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=79, score=0.836, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=80 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=80, score=0.827, total= 4.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=80 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=80, score=0.819, total= 3.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=80 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=80, score=0.827, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=80 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=80, score=0.823, total= 4.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=80 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=80, score=0.833, total= 4.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=81 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=81, score=0.827, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=81 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=81, score=0.827, total= 4.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=81 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=81, score=0.829, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=81 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=81, score=0.821, total= 5.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=81 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=81, score=0.832, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=82 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=82, score=0.827, total= 3.6s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=82 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=82, score=0.824, total= 4.3s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=82 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=82, score=0.829, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=82 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=82, score=0.821, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=82 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=82, score=0.836, total= 4.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=83 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=83, score=0.831, total= 3.4s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=83 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=83, score=0.825, total= 3.7s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=83 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=83, score=0.827, total= 4.0s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=83 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=83, score=0.828, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=83 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=83, score=0.833, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=84 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=84, score=0.826, total= 3.8s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=84 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=84, score=0.824, total= 3.9s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=84 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=84, score=0.830, total= 3.5s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=84 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=84, score=0.822, total= 4.2s [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=84 [CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=84, score=0.835, total= 3.8s
[Parallel(n_jobs=1)]: Done 420 out of 420 | elapsed: 25.0min finished
In [19]:
df1 = pd.read_csv("./P2/IncomeRF_FI.csv", header=None)
x_data = df1[0]
plt.figure(figsize = (12,8))
fig, ax1 = plt.subplots()
ax1.plot(x_data, df1[1]* 100.0, label = "Feature Importance" , color = "blue",)
ax1.axvline(x=32 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Cluster")
ax2 = ax1.twinx()
ax2.plot(x_data, df1[2]*100.0, label = "Cumulative Importance" , color = "green",)
fig.legend(loc='center left', bbox_to_anchor=(0.5, 0.5))
plt.title("Figure 6.1: Random Forest Feature Importance \nIncome Dataset")
ax1.set_xlabel("Number of Features")
ax1.set_ylabel("Feature Importance Percent")
ax2.set_ylabel("Cumulative Importance Percent")
fig.tight_layout()
plt.show()
plt.close()
<Figure size 864x576 with 0 Axes>
In [20]:
nn_results = pd.read_csv("./P2/IncomeRF_ANN.csv", header = 'infer')
nn_results = nn_results['mean_test_score'] * 100.0
#nn_train_results_pca = nn_results['mean_train_score'] * 100.0
nn_results.plot( color = 'blue', label = "Accuracy Percentage" )
plt.axvline(x=32 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Features (n=32)")
plt.legend(loc='best')
plt.title("Figure 6.2: ANN Accuracy Dimension Reduction using RF\nIncome Dataset")
plt.xlabel("Number of Features")
plt.ylabel("Accuracy Percentage");
plt.show()
plt.close()
clf = gs.best_estimator_
test_score = clf.score(X_test, Y_test)
Part 3: Clustering after reducing dimensions¶
In [21]:
dimensions_PCA = 48
dimensions_ICA = 33
dimensions_RP = 32
dimensions_RF = 32
rfc = RandomForestClassifier(n_estimators = 100, class_weight = 'balanced', random_state =5, n_jobs = -1)
algo_name = ['PCA', 'ICA', 'RP', 'RF']
filter_ = ImportanceSelect(rfc,dimensions_RF)
algos = [PCA(n_components=dimensions_PCA,random_state=10),
FastICA(n_components=dimensions_ICA,random_state=10),
SparseRandomProjection(n_components=dimensions_RP,random_state=5),
ImportanceSelect(rfc,dimensions_RF)]
for i in range(len(algos)):
if i == 3:
X2 = algos[i].fit_transform(X_train, Y_train)
else:
X2 = algos[i].fit_transform(X_train)
data2 = pd.DataFrame(np.hstack((X2,np.atleast_2d(Y_train).T)))
cols = list(range(data2.shape[1]))
cols[-1] = 'Class'
data2.columns = cols
data2.to_hdf('datasets.hdf','Income_'+algo_name[i],complib='blosc',complevel=9)
In [22]:
#random.seed(55)
titles = ["Figure 7.1: KMeans Clustering with PCA\nIncome Dataset ",
"Figure 7.2: KMeans Clustering with ICA\nIncome Dataset ",
"Figure 7.3: KMeans Clustering with RP\nIncome Dataset ",
"Figure 7.4: KMeans Clustering with RF\nIncome Dataset "]
titles_bic_1 = ["Figure 7.5: Expectation Maximization with PCA\nIncome Dataset ",
"Figure 7.6: Expectation Maximization with ICA\nIncome Dataset ",
"Figure 7.7: Expectation Maximization with RP\nIncome Dataset ",
"Figure 7.8: Expectation Maximization with RF\nIncome Dataset "]
titles_bic_2 = "Cluster Representation"
algo_name = ['PCA', 'ICA', 'RP', 'RF']
for i in range(len(algo_name)):
temp = pd.read_hdf('datasets.hdf','Income_'+algo_name[i])
#print(temp.columns)
tempX = temp.drop('Class',1).copy().values
tempY = temp['Class'].copy().values
tempX = StandardScaler().fit_transform(tempX)
KM_Silhoutte(tempX, tempY, titles[i])
compute_bic_score(tempX , titles_bic_1[i], titles_bic_2)
Index([ 0, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 26, 27, 28, 29, 30, 31,
32, 33, 34, 35, 36, 37, 38, 39,
40, 41, 42, 43, 44, 45, 46, 47,
'Class'],
dtype='object')
For n_clusters = 2 The average silhouette_score is : 0.8215261357021424
For n_clusters = 4 The average silhouette_score is : 0.3785589217345143
For n_clusters = 6 The average silhouette_score is : 0.048072447009243395
For n_clusters = 8 The average silhouette_score is : 0.04530633324794434
For n_clusters = 10 The average silhouette_score is : 0.08734565667552743
For n_clusters = 12 The average silhouette_score is : 0.09957017541125186
For n_clusters = 14 The average silhouette_score is : 0.07786173897738517
For n_clusters = 16 The average silhouette_score is : 0.05420736298294454
For n_clusters = 18 The average silhouette_score is : 0.10268903229937605
For n_clusters = 20 The average silhouette_score is : 0.06945286554344962
For n_clusters = 25 The average silhouette_score is : 0.1378228758182471
For n_clusters = 30 The average silhouette_score is : 0.15818876871088125
CV Type: spherical Components: 2 BIC Score: 3745347.508970262 CV Type: spherical Components: 4 BIC Score: 3532961.195789733 CV Type: spherical Components: 6 BIC Score: 3360142.199403949 CV Type: spherical Components: 8 BIC Score: 3081774.0951205264 CV Type: spherical Components: 10 BIC Score: 3123888.0092643755 CV Type: spherical Components: 12 BIC Score: 2869741.422831554 CV Type: spherical Components: 14 BIC Score: 3071478.101352785 CV Type: spherical Components: 16 BIC Score: 2853588.3174100206 CV Type: spherical Components: 18 BIC Score: 2615582.9497430166 CV Type: spherical Components: 20 BIC Score: 2687781.8136283876 CV Type: spherical Components: 25 BIC Score: 2470931.75167062 CV Type: spherical Components: 30 BIC Score: 2697144.886425882 CV Type: tied Components: 2 BIC Score: 4840880.209987787 CV Type: tied Components: 4 BIC Score: 4797182.67070697 CV Type: tied Components: 6 BIC Score: 4773559.5873399945 CV Type: tied Components: 8 BIC Score: 4508735.597861637 CV Type: tied Components: 10 BIC Score: 4500535.469897985 CV Type: tied Components: 12 BIC Score: 4347956.5581463305 CV Type: tied Components: 14 BIC Score: 4090163.3970648698 CV Type: tied Components: 16 BIC Score: 3745204.176464526 CV Type: tied Components: 18 BIC Score: 3464858.946825357 CV Type: tied Components: 20 BIC Score: 3555658.6005753763 CV Type: tied Components: 25 BIC Score: 3043886.1970922407 CV Type: tied Components: 30 BIC Score: 2137292.85093776 CV Type: diag Components: 2 BIC Score: 3399235.566000852 CV Type: diag Components: 4 BIC Score: 2922086.198452416 CV Type: diag Components: 6 BIC Score: 2782741.3319024527 CV Type: diag Components: 8 BIC Score: 2707080.1804200686 CV Type: diag Components: 10 BIC Score: 2649194.185964432 CV Type: diag Components: 12 BIC Score: 2623756.471771278 CV Type: diag Components: 14 BIC Score: 2369300.7315009953 CV Type: diag Components: 16 BIC Score: 2233218.823630349 CV Type: diag Components: 18 BIC Score: 1969949.4795353825 CV Type: diag Components: 20 BIC Score: 1954593.8145316672 CV Type: diag Components: 25 BIC Score: 1971297.4436673578 CV Type: diag Components: 30 BIC Score: 1980138.9712768537 CV Type: full Components: 2 BIC Score: 2166227.399620243 CV Type: full Components: 4 BIC Score: -3949920.431742895 CV Type: full Components: 6 BIC Score: -4116885.7083494337 CV Type: full Components: 8 BIC Score: 864792.9015226824 CV Type: full Components: 10 BIC Score: -1548035.1663954924 CV Type: full Components: 12 BIC Score: -6584425.064639831 CV Type: full Components: 14 BIC Score: -1059923.5098059883 CV Type: full Components: 16 BIC Score: -5330335.29175568 CV Type: full Components: 18 BIC Score: -6853798.465888098 CV Type: full Components: 20 BIC Score: -6309757.629066834 CV Type: full Components: 25 BIC Score: -4739147.330329987 CV Type: full Components: 30 BIC Score: -7668024.119338449 Lowest BIC score = -7668024.119338449
Index([ 0, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 26, 27, 28, 29, 30, 31,
32, 'Class'],
dtype='object')
For n_clusters = 2 The average silhouette_score is : 0.9646678522672069
For n_clusters = 4 The average silhouette_score is : 0.07592079643340581
For n_clusters = 6 The average silhouette_score is : 0.213497825645132
For n_clusters = 8 The average silhouette_score is : 0.072037914310071
For n_clusters = 10 The average silhouette_score is : 0.09107576272590284
For n_clusters = 12 The average silhouette_score is : 0.11480603385717252
For n_clusters = 14 The average silhouette_score is : 0.08893000622045108
For n_clusters = 16 The average silhouette_score is : 0.09890706000954294
For n_clusters = 18 The average silhouette_score is : 0.15731858877060353
For n_clusters = 20 The average silhouette_score is : 0.13820874174539804
For n_clusters = 25 The average silhouette_score is : 0.18629932228570975
For n_clusters = 30 The average silhouette_score is : 0.24555941691171776
CV Type: spherical Components: 2 BIC Score: 2794893.5705766655 CV Type: spherical Components: 4 BIC Score: 2666504.704999755 CV Type: spherical Components: 6 BIC Score: 2505135.568666979 CV Type: spherical Components: 8 BIC Score: 2433314.9162478237 CV Type: spherical Components: 10 BIC Score: 2477678.9921725243 CV Type: spherical Components: 12 BIC Score: 2449741.55101997 CV Type: spherical Components: 14 BIC Score: 2191846.4963730136 CV Type: spherical Components: 16 BIC Score: 2111020.322212734 CV Type: spherical Components: 18 BIC Score: 2209041.0056039 CV Type: spherical Components: 20 BIC Score: 2032230.648118387 CV Type: spherical Components: 25 BIC Score: 1788065.267311982 CV Type: spherical Components: 30 BIC Score: 1783151.3680130304 CV Type: tied Components: 2 BIC Score: 3355552.7944893087 CV Type: tied Components: 4 BIC Score: 3315336.9927786887 CV Type: tied Components: 6 BIC Score: 2955079.498268905 CV Type: tied Components: 8 BIC Score: 2873092.346268292 CV Type: tied Components: 10 BIC Score: 2610782.9816783145 CV Type: tied Components: 12 BIC Score: 2516077.3923429614 CV Type: tied Components: 14 BIC Score: 2517671.7818871518 CV Type: tied Components: 16 BIC Score: 2282096.8191429265 CV Type: tied Components: 18 BIC Score: 2093510.9837368752 CV Type: tied Components: 20 BIC Score: 1870023.9688724468 CV Type: tied Components: 25 BIC Score: 1433636.4387195671 CV Type: tied Components: 30 BIC Score: 1269060.5897719325 CV Type: diag Components: 2 BIC Score: 1964898.324173375 CV Type: diag Components: 4 BIC Score: 1625543.2344392585 CV Type: diag Components: 6 BIC Score: 1398518.6652593124 CV Type: diag Components: 8 BIC Score: 1115351.0092439002 CV Type: diag Components: 10 BIC Score: 1165605.664829789 CV Type: diag Components: 12 BIC Score: 1029835.2559820729 CV Type: diag Components: 14 BIC Score: 645806.0061501007 CV Type: diag Components: 16 BIC Score: 635139.8559461713 CV Type: diag Components: 18 BIC Score: 634607.3325814724 CV Type: diag Components: 20 BIC Score: 372034.0620048925 CV Type: diag Components: 25 BIC Score: 198045.7241070556 CV Type: diag Components: 30 BIC Score: 22892.612583831127 CV Type: full Components: 2 BIC Score: 1053404.9812793732 CV Type: full Components: 4 BIC Score: 1142072.3111420078 CV Type: full Components: 6 BIC Score: -2772026.1818931396 CV Type: full Components: 8 BIC Score: -1737262.134795803 CV Type: full Components: 10 BIC Score: -3046955.8111493406 CV Type: full Components: 12 BIC Score: -2471378.144497045 CV Type: full Components: 14 BIC Score: -2720490.9954466717 CV Type: full Components: 16 BIC Score: -3285546.2105115782 CV Type: full Components: 18 BIC Score: -3927278.8772990536 CV Type: full Components: 20 BIC Score: -4114532.0709881526 CV Type: full Components: 25 BIC Score: -3843988.980123459 CV Type: full Components: 30 BIC Score: -4420697.066712804 Lowest BIC score = -4420697.066712804
Index([ 0, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 26, 27, 28, 29, 30, 31,
'Class'],
dtype='object')
For n_clusters = 2 The average silhouette_score is : 0.16022875673042225
For n_clusters = 4 The average silhouette_score is : 0.15969012342995884
For n_clusters = 6 The average silhouette_score is : 0.15536780189221971
For n_clusters = 8 The average silhouette_score is : 0.09753844411831471
For n_clusters = 10 The average silhouette_score is : 0.0952952534361466
For n_clusters = 12 The average silhouette_score is : 0.10272294301390972
For n_clusters = 14 The average silhouette_score is : 0.10893886515134017
For n_clusters = 16 The average silhouette_score is : 0.09367900790452478
For n_clusters = 18 The average silhouette_score is : 0.12388388890315521
For n_clusters = 20 The average silhouette_score is : 0.12790969312503994
For n_clusters = 25 The average silhouette_score is : 0.1332935713527438
For n_clusters = 30 The average silhouette_score is : 0.15967340153296714
CV Type: spherical Components: 2 BIC Score: 2469005.22954279 CV Type: spherical Components: 4 BIC Score: 2210724.047029868 CV Type: spherical Components: 6 BIC Score: 2124578.46914328 CV Type: spherical Components: 8 BIC Score: 1990241.9800328682 CV Type: spherical Components: 10 BIC Score: 2026840.3656783383 CV Type: spherical Components: 12 BIC Score: 1915772.7693289593 CV Type: spherical Components: 14 BIC Score: 1842523.769251369 CV Type: spherical Components: 16 BIC Score: 1805533.5327718926 CV Type: spherical Components: 18 BIC Score: 1816137.4260121973 CV Type: spherical Components: 20 BIC Score: 1732167.9627771338 CV Type: spherical Components: 25 BIC Score: 1767862.1836157956 CV Type: spherical Components: 30 BIC Score: 1580775.6728847506 CV Type: tied Components: 2 BIC Score: 2923609.346985227 CV Type: tied Components: 4 BIC Score: 2885791.2198778247 CV Type: tied Components: 6 BIC Score: 2819918.7557755043 CV Type: tied Components: 8 BIC Score: 2762697.7251161733 CV Type: tied Components: 10 BIC Score: 2717207.7802632772 CV Type: tied Components: 12 BIC Score: 2622475.5699871015 CV Type: tied Components: 14 BIC Score: 2587420.648719321 CV Type: tied Components: 16 BIC Score: 2519339.441507217 CV Type: tied Components: 18 BIC Score: 2470754.053737411 CV Type: tied Components: 20 BIC Score: 2397490.8458128436 CV Type: tied Components: 25 BIC Score: 2256154.7829355323 CV Type: tied Components: 30 BIC Score: 2040743.6783353519 CV Type: diag Components: 2 BIC Score: 1561396.6396238785 CV Type: diag Components: 4 BIC Score: -34207.82500506729 CV Type: diag Components: 6 BIC Score: -76772.76057003971 CV Type: diag Components: 8 BIC Score: -34742.3297100728 CV Type: diag Components: 10 BIC Score: -716663.9759328412 CV Type: diag Components: 12 BIC Score: -1235531.5963014266 CV Type: diag Components: 14 BIC Score: -1190866.7878785722 CV Type: diag Components: 16 BIC Score: -1225442.4099257055 CV Type: diag Components: 18 BIC Score: -1832656.3498493833 CV Type: diag Components: 20 BIC Score: -1482871.5102674135 CV Type: diag Components: 25 BIC Score: -1419408.6493819994 CV Type: diag Components: 30 BIC Score: -2373935.433847721 CV Type: full Components: 2 BIC Score: -68776.4115549257 CV Type: full Components: 4 BIC Score: -656825.6801409031 CV Type: full Components: 6 BIC Score: -1933115.5191570814 CV Type: full Components: 8 BIC Score: -1905070.445373736 CV Type: full Components: 10 BIC Score: -1882612.208893617 CV Type: full Components: 12 BIC Score: -3229480.4135010685 CV Type: full Components: 14 BIC Score: -3070459.008296061 CV Type: full Components: 16 BIC Score: -2998327.881747128 CV Type: full Components: 18 BIC Score: -3902800.0141554875 CV Type: full Components: 20 BIC Score: -2810025.759273552 CV Type: full Components: 25 BIC Score: -3838681.8899121713 CV Type: full Components: 30 BIC Score: -4467423.763978708 Lowest BIC score = -4467423.763978708
Index([ 0, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 26, 27, 28, 29, 30, 31,
'Class'],
dtype='object')
For n_clusters = 2 The average silhouette_score is : 0.12169582291600911
For n_clusters = 4 The average silhouette_score is : 0.14022899111373643
For n_clusters = 6 The average silhouette_score is : 0.15746669528328971
For n_clusters = 8 The average silhouette_score is : 0.1805351940015467
For n_clusters = 10 The average silhouette_score is : 0.14735435240340997
For n_clusters = 12 The average silhouette_score is : 0.15222477543125723
For n_clusters = 14 The average silhouette_score is : 0.1484213859648749
For n_clusters = 16 The average silhouette_score is : 0.18957531078729173
For n_clusters = 18 The average silhouette_score is : 0.1781551968468422
For n_clusters = 20 The average silhouette_score is : 0.19399015395613495
For n_clusters = 25 The average silhouette_score is : 0.20856161952279353
For n_clusters = 30 The average silhouette_score is : 0.23067453495515897
CV Type: spherical Components: 2 BIC Score: 3155204.028818856 CV Type: spherical Components: 4 BIC Score: 3025094.1382223754 CV Type: spherical Components: 6 BIC Score: 2946091.7199722263 CV Type: spherical Components: 8 BIC Score: 2848359.6696788864 CV Type: spherical Components: 10 BIC Score: 2776518.417764349 CV Type: spherical Components: 12 BIC Score: 2619968.1231891536 CV Type: spherical Components: 14 BIC Score: 2621102.596302614 CV Type: spherical Components: 16 BIC Score: 2515116.1417451296 CV Type: spherical Components: 18 BIC Score: 2534939.2915649754 CV Type: spherical Components: 20 BIC Score: 2344056.39136436 CV Type: spherical Components: 25 BIC Score: 2255135.688826514 CV Type: spherical Components: 30 BIC Score: 2185987.6409853664 CV Type: tied Components: 2 BIC Score: 2064502.2242727114 CV Type: tied Components: 4 BIC Score: 1546255.483639819 CV Type: tied Components: 6 BIC Score: 1442988.2537666245 CV Type: tied Components: 8 BIC Score: 1325196.940344845 CV Type: tied Components: 10 BIC Score: 798976.2275015559 CV Type: tied Components: 12 BIC Score: 1004792.2816102488 CV Type: tied Components: 14 BIC Score: 1088544.4989831918 CV Type: tied Components: 16 BIC Score: -237818.25770603583 CV Type: tied Components: 18 BIC Score: -987250.4294325517 CV Type: tied Components: 20 BIC Score: -922237.3717277922 CV Type: tied Components: 25 BIC Score: -903693.9938874283 CV Type: tied Components: 30 BIC Score: -962263.6691750985 CV Type: diag Components: 2 BIC Score: 922523.4044359996 CV Type: diag Components: 4 BIC Score: -138700.1425964475 CV Type: diag Components: 6 BIC Score: -988054.457217085 CV Type: diag Components: 8 BIC Score: -2096796.990857835 CV Type: diag Components: 10 BIC Score: -2665228.039344091 CV Type: diag Components: 12 BIC Score: -3234841.4290826647 CV Type: diag Components: 14 BIC Score: -4052055.7650737884 CV Type: diag Components: 16 BIC Score: -4127713.1138305124 CV Type: diag Components: 18 BIC Score: -4766081.457214911 CV Type: diag Components: 20 BIC Score: -4659057.469583905 CV Type: diag Components: 25 BIC Score: -5258364.160233665 CV Type: diag Components: 30 BIC Score: -5677458.393623538 CV Type: full Components: 2 BIC Score: 418826.656334546 CV Type: full Components: 4 BIC Score: -820295.0824815511 CV Type: full Components: 6 BIC Score: -2042795.9160205168 CV Type: full Components: 8 BIC Score: -1764055.5297574208 CV Type: full Components: 10 BIC Score: -3569718.6112716575 CV Type: full Components: 12 BIC Score: -3863197.571373438 CV Type: full Components: 14 BIC Score: -4430588.0835696645 CV Type: full Components: 16 BIC Score: -4752320.395185009 CV Type: full Components: 18 BIC Score: -5027715.870817529 CV Type: full Components: 20 BIC Score: -5358760.149064392 CV Type: full Components: 25 BIC Score: -5695101.185917668 CV Type: full Components: 30 BIC Score: -6171014.81591099 Lowest BIC score = -6171014.81591099
In [32]:
rc_results = pd.read_csv("./P2/IncomeRP_Reconstruction.csv", header = 'infer')
rc_results = rc_results['Reconstruction'] * 100.0
rc_results.plot( color = 'orange', label = "Reconstruction Error" )
plt.axvline(x=33 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Components (n=33)")
plt.legend(loc='best')
plt.title("Figure 5.2: Random Projection Reconstruction Error\nIncome Dataset")
plt.xlabel("Random Components")
plt.ylabel("Reconstruction Error %");
plt.show()
plt.close()
In [ ]: